Time-efficient and complete coverage path planning based on flow networks for multi-robots

Complete coverage path planning (CCPP), specifically, the efficiency and completeness of coverage of robots, is one of the major problems in autonomous mobile robotics. This study proposes a path planning technique to solve global time optimization. Conventional algorithms related to template-based coverage can minimize the time required to cover particular cells. The minimal turning path is mostly based on the shape and size of the cell. Conventional algorithms can determine the optimum time path inside a cell; however, these algorithms cannot ensure that the total time determined for the coverage path is the global optimum. This study presents an algorithm that can convert a CCPP problem into a flow network by exact cell decomposition. The total time cost to reach the edge of a flow network is the sum of the time to cover the current cell and the time to shift in adjacent cells. The time cost determines a minimum-cost path from the start node to the final node through the flow network, which is capable of visiting each node exactly once through the network search algorithm. Search results show that the time-efficient coverage can obtain the global optimum. Simulation and experimental results demonstrate that the proposed algorithm operates in a time-efficient manner.